---
title: "NOAA"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(p8105.datasets)
library(plotly)
data("ny_noaa")
```

Column {data-width=650}
-----------------------------------------------------------------------

### Comparsion of three cities airport maximum and minmum temperature

```{r}
ny_noaa=ny_noaa%>% 
  janitor::clean_names() %>% 
  drop_na() %>% 
   mutate(
    location = recode(
      id, 
      USW00014732 = "Laguardia Airport",
      USW00014771 = "Syracuse Hancock Airport",
      USW00094789 = "JFK Airport"),
    tmin = as.numeric(tmin)/10 ,
    tmax = as.numeric(tmax)/10 ) %>% 
  filter(id %in% c("USW00014732", "USW00014771", "USW00094789")) %>%  
  mutate(year = lubridate::year(date)) 

ny_noaa %>%
  select(location,date,tmin,tmax)%>%
  mutate(text_label = str_c("\nMax temperature: ", tmax, "\nMin temperaure: ", tmin,"\nLocation: ",location)) %>% 
  plot_ly(
    x = ~tmin, y = ~tmax, type = "scatter", mode = "markers",
    color = ~location, text = ~text_label, alpha = 0.7)
```

Column {data-width=350}
-----------------------------------------------------------------------

### Comparsion of three cities airport mean max-temperature in celsius by year

```{r}
mean_tmax = 
  ny_noaa %>% 
  group_by(year, location) %>% 
  summarise(mean_tmax = mean(tmax))

plot = 
  ggplot(mean_tmax, aes(x = year, y = mean_tmax, color = location)) + 
  geom_line() +
  ylab('Mean Max temperature in celsius') 
ggplotly(plot) 
```

### Distribution of three cities airport annual snowfall for 10 locations between 1981 and 2010.

```{r}
ny_noaa %>%
  group_by(location, year) %>%
  summarize(ysnow = sum(snow)) %>%
  mutate(location = fct_reorder(location, ysnow)) %>% 
  plot_ly(y = ~ysnow, color = ~location, type = "box", colors = "viridis") %>% 
  layout(
    xaxis = list(title = "Location", tickangle = 50),
    yaxis = list(title = "Annual Snowfall"))
```